{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:34:29Z","timestamp":1776976469755,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>The dynamic changes in the warehousing environment, the heterogeneity of task allocation, and the complexity of multi-agent collaboration make it difficult for traditional scheduling algorithms to meet the challenges of modern warehousing. This study proposes a heterogeneous multi-agent collaborative scheduling method based on an improved Proximal Policy Optimization (PPO) framework, which integrates a hierarchical attention-driven architecture and dynamic variance constraint algorithm to address the spatio-temporal coupling constraint problem of distributed warehousing scheduling. We designed a multi-objective reward function (considering task timeliness, energy consumption, and space utilization) and a dynamic computing resource allocation strategy to enhance the system\u2019s efficiency and robustness in handling large-scale orders (500+ daily orders) and unexpected situations (e.g., equipment failure). Experimental data shows that compared with traditional scheduling algorithms, the completion rate of agent collaborative tasks has increased from 56.89% to 73.24%, the average task execution delay has dropped from 22.1 seconds to 15.67 seconds, and the storage space utilization rate has increased from 49.2% to 63.5%. In complex order scenarios, the framework's sorting accuracy rate for multiple types of goods reaches 94.5%, which is 37.67 percentage points higher than the baseline model, and the proportion of multi-agent communication overhead in system resources has dropped from 88.76% to 63.5%, which verifies the algorithm's optimization capabilities under resource constraints.<\/jats:p>","DOI":"10.31449\/inf.v50i11.12435","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:17Z","timestamp":1776972977000},"source":"Crossref","is-referenced-by-count":0,"title":["A Hierarchical Attention-Based Heterogeneous Multi-Agent PPO Framework for Distributed Warehouse Scheduling"],"prefix":"10.31449","volume":"50","author":[{"given":"Yuzhang","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,23]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12435\/6656","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12435\/6656","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:17Z","timestamp":1776972977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,4,23]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i11.12435","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}